Structure learning is a process in machine learning and statistics that involves discovering the underlying structure of a probabilistic model from data, typically focusing on identifying dependencies among variables. It is crucial in domains like Bayesian networks and graphical models, where understanding the relationships among variables can lead to better predictions and insights about the data-generating process.